To determine trust in model predictions, current works
typically use (a) explainability methods (XAI) [1]}, [2]}, [3]}; which, though explainable, post-hoc and suitable for providing qualitative insights, require humans to oversee the explanations and are not directly actionable for continuous and automated model monitoring; or (b) uncertainty estimation techniques [4]}, [5]}, [6]}, [7]}, [8]}, [9]};
that are difficult to train or are computationally expensive. They are frequently non-post-hoc, requiring complicated modifications to training strategies and model architectures. Some even require exposure to labelled outlier data during training which are often unavailable while many do not scale well with input-dimensionality. And, as we'll show later in this paper (Section REF ), most of them are insensitive to input semantic content, failing to perform well as a trust scoring framework.
